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Connectionist Large Vocabulary Speech Recognition

  • Conference paper
Speech Recognition and Understanding

Part of the book series: NATO ASI Series ((NATO ASI F,volume 75))

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Abstract

In this paper, the problem of large vocabulary word recognition is addressed from a connectionist perspective. The problem is not only of practical interest but also of scientific importance, since a workable solution must integrate pattern recognition under consideration of sequential, symbolic constraints. We have developed two large vocabulary word recognition systems based on different speech recognition philosophies. One of the systems exploits the power of neural networks in performing accurate classification, the other the power of producing good non-linear function approximation and signal prediction. We present each system’s operation and evaluate its performance. Both achieved respectable recognition scores in excess of 90% correct for vocabularies of up to 5000 words. We suggest further avenues towards improvement of either system and in the process discuss the relative strengths of either approach.

We gratefully acknowledge IEICE for permission to reprint this paper. It has originally appeared in the journal of the IEICE, Vol.J73-D-II, No.8 pp. 1122–1131, (Aug. 1990) by the same author.

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Author information

Authors and Affiliations

  1. School of Computer Science, Carnegie-Mellon University, Pittsburgh, PA, 15213, USA

    Alex Waibel

Authors
  1. Alex Waibel
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Editor information

Editors and Affiliations

  1. Dipartimento di Automatica e Informatica, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129, Torino, Italy

    Pietro Laface

  2. School of Computer Science, 3480 University St., Montreal, Quebec, H3A 2A7, Canada

    Renato De Mori

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© 1992 Springer-Verlag Berlin Heidelberg

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Waibel, A. (1992). Connectionist Large Vocabulary Speech Recognition. In: Laface, P., De Mori, R. (eds) Speech Recognition and Understanding. NATO ASI Series, vol 75. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-76626-8_28

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  • DOI: https://doi.org/10.1007/978-3-642-76626-8_28

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-76628-2

  • Online ISBN: 978-3-642-76626-8

  • eBook Packages: Springer Book Archive

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